A variety of techniques and apparatus have been used to satisfy the requirements of automated currency handling systems. At the lower end of sophistication in this area of technology are systems capable of handling only a specific type of currency, such as a specific dollar denomination, while rejecting all other currency types. At the upper end are complex systems which are capable of identifying and discriminating among multiple currency denominations.
Currency discrimination systems typically employ either magnetic sensing or optical sensing for discriminating between different currency denominations. Magnetic sensing is based on detecting the presence or absence of magnetic ink in portions of the printed indicia on the currency by using magnetic sensors, usually ferrite core-based sensors, and using the detected magnetic signals, after undergoing analog or digital processing, as the basis for currency discrimination. The more commonly used optical sensing technique, on the other hand, is based on detecting and analyzing variations in light reflectance or transmissivity characteristics occurring when a currency bill is illuminated and scanned by a strip of focused light. The subsequent currency discrimination is based on the comparison of sensed optical characteristics with prestored parameters for different currency denominations, while accounting for adequate tolerances reflecting differences among individual bills of a given denomination.
A major obstacle in implementing automated currency discrimination systems is obtaining an optimum compromise between the criteria used to adequately define the characteristic pattern for a particular currency denomination and the time required to analyze test data and compare it to predefined parameters in order to identify the currency bill under scrutiny. Even with the use of microprocessors for processing the test data resulting from the scanning of a bill, a finite amount of time is required for acquiring samples and for the process of comparing the test data to stored parameters to identify the denomination of the bill.
Most of the optical scanning systems available today utilize complex algorithms for obtaining a large number of reflectance data samples as a currency bill is scanned by an optical scanhead and for subsequently comparing the data to corresponding stored parameters to identify the bill denomination. Conventional systems require a relatively large number of optical samples per bill scan in order to sufficiently discriminate between currency denominations, particularly those denominations for which the reflectance patterns are not markedly distinguishable. The use of the large number of data samples slows down the rate at which incoming bills may be scanned and, more importantly, requires a correspondingly longer period of time to process the data in accordance with the discrimination algorithm.
The end result is that systems capable of accurate currency discrimination are costly and generally incapable of currency discrimination at high speeds with a high degree of accuracy.